{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.162654Z",
     "start_time": "2025-08-14T06:25:16.017952Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "# 完全禁用GPU以避免CUDA相关错误\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n",
    "from IPython.display import Image,display\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from PIL import ImageOps"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 图像位置\n",
    "input_dir = 'd:/project/dataset/segdata/images'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.169102Z",
     "start_time": "2025-08-14T06:25:18.164638Z"
    }
   },
   "id": "ab38173e310bd894",
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 图像路径\n",
    "input_img_path = sorted([os.path.join(input_dir, fname)\n",
    "                         for fname in os.listdir(input_dir) if fname.endswith('.jpg')])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.214734Z",
     "start_time": "2025-08-14T06:25:18.170590Z"
    }
   },
   "id": "9932808f6138b976",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
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'd:/project/dataset/segdata/images\\\\Bombay_200.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_201.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_202.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_203.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_204.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_205.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_206.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_208.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_209.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_21.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_210.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_213.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_214.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_215.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_217.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_22.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_220.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_221.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_23.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_24.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_25.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_26.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_27.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_29.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_3.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_30.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_31.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_32.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_33.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_34.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_36.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_37.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_38.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_39.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_4.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_40.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_41.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_42.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_43.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_45.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_46.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_47.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_48.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_49.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_5.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_50.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_52.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_53.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_54.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_55.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_56.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_57.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_58.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_59.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_6.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_60.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_61.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_62.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_63.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_64.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_65.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_66.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_67.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_68.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_69.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_7.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_70.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_71.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_72.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_73.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_74.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_75.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_76.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_77.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_78.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_79.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_8.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_80.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_81.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_82.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_83.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_84.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_85.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_86.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_87.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_88.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_89.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_9.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_90.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_91.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_92.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_93.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_94.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_95.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_96.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_97.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_98.jpg',\n 'd:/project/dataset/segdata/images\\\\Bombay_99.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_10.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_100.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_101.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_102.jpg',\n 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'd:/project/dataset/segdata/images\\\\British_Shorthair_117.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_118.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_119.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_120.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_121.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_122.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_123.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_124.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_125.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_126.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_127.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_128.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_129.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_130.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_133.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_134.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_135.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_136.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_137.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_139.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_140.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_141.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_144.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_145.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_148.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_149.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_15.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_151.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_153.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_154.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_155.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_156.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_157.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_158.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_159.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_16.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_160.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_161.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_162.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_163.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_164.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_165.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_166.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_167.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_168.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_169.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_17.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_170.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_172.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_173.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_174.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_175.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_176.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_177.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_178.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_179.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_18.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_180.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_181.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_182.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_183.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_184.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_185.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_186.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_187.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_188.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_189.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_190.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_193.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_195.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_196.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_197.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_198.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_199.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_2.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_200.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_201.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_203.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_204.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_205.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_207.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_209.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_21.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_210.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_212.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_213.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_218.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_22.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_223.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_230.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_239.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_241.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_248.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_25.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_258.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_26.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_263.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_265.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_266.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_267.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_268.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_269.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_27.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_270.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_271.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_272.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_273.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_274.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_275.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_276.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_277.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_278.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_28.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_29.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_3.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_30.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_31.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_32.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_33.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_34.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_36.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_37.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_38.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_39.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_40.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_41.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_42.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_43.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_44.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_45.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_46.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_47.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_48.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_49.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_50.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_51.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_52.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_53.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_54.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_55.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_56.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_57.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_58.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_59.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_6.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_60.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_61.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_62.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_63.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_64.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_65.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_66.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_67.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_68.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_70.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_71.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_72.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_74.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_75.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_77.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_78.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_79.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_8.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_82.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_83.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_84.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_85.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_86.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_87.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_88.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_89.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_9.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_90.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_91.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_92.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_93.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_94.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_95.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_96.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_97.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_98.jpg',\n 'd:/project/dataset/segdata/images\\\\British_Shorthair_99.jpg',\n ...]"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_img_path"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.237054Z",
     "start_time": "2025-08-14T06:25:18.216222Z"
    }
   },
   "id": "b6fec6eaad680bd6",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 标注信息\n",
    "target_dir = 'd:/project/dataset/segdata/annotations/trimaps/'\n",
    "# 目标值\n",
    "target_img_path = sorted([os.path.join(target_dir, fname) for fname in os.listdir(\n",
    "    target_dir) if fname.endswith('.png') and not fname.startswith('.')])\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.288638Z",
     "start_time": "2025-08-14T06:25:18.239535Z"
    }
   },
   "id": "f488caf9c3a71f9",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "img_size = (160,160)\n",
    "batch_size = 32\n",
    "num_classes = 4"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.294590Z",
     "start_time": "2025-08-14T06:25:18.290126Z"
    }
   },
   "id": "cba9621af0a8cc1a",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(input_img_path[5000]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.312447Z",
     "start_time": "2025-08-14T06:25:18.296079Z"
    }
   },
   "id": "4f5b66a9ccdbfecc",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "img = ImageOps.autocontrast(load_img(target_img_path[5000]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.327326Z",
     "start_time": "2025-08-14T06:25:18.313438Z"
    }
   },
   "id": "8e8e4e5576122438",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<PIL.Image.Image image mode=RGB size=396x500>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(img)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.341214Z",
     "start_time": "2025-08-14T06:25:18.328814Z"
    }
   },
   "id": "bf06019ce50fc14a",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.image import load_img"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.346670Z",
     "start_time": "2025-08-14T06:25:18.342703Z"
    }
   },
   "id": "ad775b9b7eca4707",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "class OxfordPets(keras.utils.Sequence):\n",
    "    # 初始化\n",
    "    def __init__(self,batch_size,img_size,input_img_paths,target_img_paths):\n",
    "        # 批次大小\n",
    "        self.batch_size = batch_size\n",
    "        # 图像大小\n",
    "        self.img_size = img_size\n",
    "        # 图像的路径\n",
    "        self.input_img_paths = input_img_paths\n",
    "        # 目标值路经\n",
    "        self.target_img_paths = target_img_paths        \n",
    "\n",
    "    # 迭代次数\n",
    "    def __len__(self):\n",
    "        return len(self.target_img_paths)//self.batch_size\n",
    "\n",
    "    # 获取batch数据\n",
    "    def __getitem__(self,idx):\n",
    "        # 获取该批次对应的样本的索引\n",
    "        i = idx * self.batch_size\n",
    "        # 获取该批次数据\n",
    "        batch_input_img_paths = self.input_img_paths[i:i+self.batch_size]\n",
    "        batch_target_img_paths = self.target_img_paths[i:i+self.batch_size]\n",
    "        # 构建特征值\n",
    "        x = np.zeros((batch_size,)+self.img_size+(3,),dtype=\"float32\")\n",
    "        for j,path in enumerate(batch_input_img_paths):\n",
    "            img = load_img(path,target_size=self.img_size)\n",
    "            x[j] = img\n",
    "        # 构建目标值\n",
    "        y = np.zeros((batch_size,)+self.img_size+(1,),dtype='uint8')\n",
    "        for j,path in enumerate(batch_target_img_paths):\n",
    "            img = load_img(path,target_size=self.img_size,color_mode='grayscale')\n",
    "            y[j] = np.expand_dims(img,2)\n",
    "        return x,y"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.356094Z",
     "start_time": "2025-08-14T06:25:18.348158Z"
    }
   },
   "id": "2abffead8ebf0618",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose\n",
    "from tensorflow.keras.layers import MaxPooling2D, Cropping2D, Concatenate\n",
    "from tensorflow.keras.layers import Lambda, Activation, BatchNormalization, Dropout\n",
    "from tensorflow.keras.models import Model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.363038Z",
     "start_time": "2025-08-14T06:25:18.357582Z"
    }
   },
   "id": "ab2b5086320572c8",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def downsampling_block(input_tensor,filters):\n",
    "    # 输入：input_tensor,通道数：filters\n",
    "    # 卷积\n",
    "    x = Conv2D(filters,kernel_size=(3,3),padding='same')(input_tensor)\n",
    "    # BN \n",
    "    x = BatchNormalization()(x)\n",
    "    # 激活\n",
    "    x = Activation('relu')(x)\n",
    "    # 卷积\n",
    "    x = Conv2D(filters,kernel_size=(3,3),padding='same')(x)\n",
    "    # BN \n",
    "    x = BatchNormalization()(x)\n",
    "    # 激活\n",
    "    x = Activation('relu')(x)\n",
    "    # 返回\n",
    "    return MaxPooling2D(pool_size=(2,2))(x),x\n",
    "\n",
    "def upsampling_block(input_tensor,skip_tensor,filters):\n",
    "    # input——tensor:输入特征图，skip_tensor:编码部分的特征图，filters:通道数\n",
    "    # 反卷积\n",
    "    x = Conv2DTranspose(filters,kernel_size=(2,2),strides=(2,2),padding='same')(input_tensor)\n",
    "    # 尺寸\n",
    "    _,x_height,x_width,_ = x.shape\n",
    "    _,s_height,s_width,_ = skip_tensor.shape\n",
    "    # 计算差异\n",
    "    h_crop = s_height-x_height\n",
    "    w_crop = s_width-x_width\n",
    "    # 判断是否进行裁剪\n",
    "    if h_crop==0 and w_crop ==0:\n",
    "        y = skip_tensor\n",
    "    else:\n",
    "        # 获取裁剪的大小\n",
    "        cropping = ((h_crop//2,h_crop-h_crop//2),(w_crop//2,w_crop-w_crop//2))\n",
    "        y = Cropping2D(cropping=cropping)(skip_tensor)\n",
    "    # 特征融合\n",
    "    x = Concatenate()([x,y])\n",
    "    # 卷积\n",
    "    x = Conv2D(filters,kernel_size=(3,3),padding='same')(x)\n",
    "    # BN \n",
    "    x = BatchNormalization()(x)\n",
    "    # 激活层\n",
    "    x = Activation('relu')(x)\n",
    "    # 卷积\n",
    "    x = Conv2D(filters,kernel_size=(3,3),padding='same')(x)\n",
    "    # BN \n",
    "    x = BatchNormalization()(x)\n",
    "    # 激活层\n",
    "    x = Activation('relu')(x)\n",
    "    return x"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.373454Z",
     "start_time": "2025-08-14T06:25:18.364030Z"
    }
   },
   "id": "565ee5f6a0a0f0a6",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def unet(imagesize,classes,fetures=64,depth=3):\n",
    "    # 定义输入\n",
    "    inputs = keras.Input(shape=(imagesize+(3,)))\n",
    "    x = inputs\n",
    "    # 构建编码部分\n",
    "    skips = []\n",
    "    for i in range(depth):\n",
    "        x,x0 = downsampling_block(x,fetures)\n",
    "        skips.append(x0)\n",
    "        fetures *=2\n",
    "    # 卷积\n",
    "    x = Conv2D(filters=fetures,kernel_size=(3,3),padding='same')(x)\n",
    "    # BN\n",
    "    x = BatchNormalization()(x)\n",
    "    # 激活\n",
    "    x = Activation('relu')(x)\n",
    "    # 卷积\n",
    "    x = Conv2D(filters=fetures,kernel_size=(3,3),padding='same')(x)\n",
    "    # BN\n",
    "    x = BatchNormalization()(x)\n",
    "    # 激活\n",
    "    x = Activation('relu')(x)\n",
    "    # 解码部分\n",
    "    for i in reversed(range(depth)):\n",
    "        fetures //=2\n",
    "        x = upsampling_block(x,skips[i],fetures)\n",
    "    # 1x1卷积\n",
    "    x = Conv2D(filters= classes,kernel_size=(1,1),padding='same')(x)\n",
    "    # 激活\n",
    "    outputs = Activation('softmax')(x)\n",
    "    return keras.Model(inputs=inputs,outputs = outputs)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.383374Z",
     "start_time": "2025-08-14T06:25:18.376926Z"
    }
   },
   "id": "8fb52887a3c8c840",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "model = unet(img_size,num_classes)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:18.939390Z",
     "start_time": "2025-08-14T06:25:18.384862Z"
    }
   },
   "id": "a09f092ff2d35224",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 160, 160, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 160, 160, 64  1792        ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 160, 160, 64  256        ['conv2d[0][0]']                 \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 160, 160, 64  0           ['batch_normalization[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 160, 160, 64  36928       ['activation[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 160, 160, 64  256        ['conv2d_1[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 160, 160, 64  0           ['batch_normalization_1[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 80, 80, 64)   0           ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 80, 80, 128)  73856       ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 80, 80, 128)  512        ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 80, 80, 128)  0           ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 80, 80, 128)  147584      ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 80, 80, 128)  512        ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 80, 80, 128)  0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 40, 40, 128)  0          ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 40, 40, 256)  295168      ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 40, 40, 256)  1024       ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 40, 40, 256)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 40, 40, 256)  590080      ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 40, 40, 256)  1024       ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 40, 40, 256)  0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 20, 20, 256)  0          ['activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 20, 20, 512)  1180160     ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 20, 20, 512)  2048       ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 20, 20, 512)  0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 20, 20, 512)  2359808     ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 20, 20, 512)  2048       ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 20, 20, 512)  0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTransp  (None, 40, 40, 256)  524544     ['activation_7[0][0]']           \n",
      " ose)                                                                                             \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 40, 40, 512)  0           ['conv2d_transpose[0][0]',       \n",
      "                                                                  'activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 40, 40, 256)  1179904     ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 40, 40, 256)  1024       ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 40, 40, 256)  0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 40, 40, 256)  590080      ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 40, 40, 256)  1024       ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 40, 40, 256)  0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_transpose_1 (Conv2DTran  (None, 80, 80, 128)  131200     ['activation_9[0][0]']           \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 80, 80, 256)  0           ['conv2d_transpose_1[0][0]',     \n",
      "                                                                  'activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 80, 80, 128)  295040      ['concatenate_1[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 80, 80, 128)  512        ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 80, 80, 128)  0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 80, 80, 128)  147584      ['activation_10[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 80, 80, 128)  512        ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 80, 80, 128)  0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_transpose_2 (Conv2DTran  (None, 160, 160, 64  32832      ['activation_11[0][0]']          \n",
      " spose)                         )                                                                 \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 160, 160, 12  0           ['conv2d_transpose_2[0][0]',     \n",
      "                                8)                                'activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 160, 160, 64  73792       ['concatenate_2[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 160, 160, 64  256        ['conv2d_12[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 160, 160, 64  0           ['batch_normalization_12[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 160, 160, 64  36928       ['activation_12[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 160, 160, 64  256        ['conv2d_13[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 160, 160, 64  0           ['batch_normalization_13[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 160, 160, 4)  260         ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 160, 160, 4)  0           ['conv2d_14[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 7,708,804\n",
      "Trainable params: 7,703,172\n",
      "Non-trainable params: 5,632\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:19.005853Z",
     "start_time": "2025-08-14T06:25:18.940878Z"
    }
   },
   "id": "bb7c55a7322a0bad",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import random\n",
    "# 验证集数量\n",
    "val_samples = 1000\n",
    "# 打乱\n",
    "random.Random(1337).shuffle(input_img_path)\n",
    "random.Random(1337).shuffle(target_img_path)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "131d165bdda434b",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 划分数据集\n",
    "# 训练集\n",
    "train_input_img_paths = input_img_path[:-val_samples]\n",
    "train_target_img_paths = target_img_path[:-val_samples]\n",
    "# 验证集\n",
    "val_input_img_paths = input_img_path[-val_samples:]\n",
    "val_target_img_paths = target_img_path[-val_samples:]\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ac0ef6f3e67c71b9",
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "train_gen = OxfordPets(batch_size,img_size,train_input_img_paths,train_target_img_paths)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8d0fb10154c134a6",
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "val_gen = OxfordPets(batch_size,img_size,val_input_img_paths,val_target_img_paths)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "30102ad7164f6abd",
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',loss=\"sparse_categorical_crossentropy\")"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ab45abf1dd329b03",
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000001E88A08B158> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000001E88A08B158> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "1/1 [==============================] - ETA: 0s - loss: 1.9541WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000001E890B0F598> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000001E890B0F598> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "1/1 [==============================] - 19s 19s/step - loss: 1.9541 - val_loss: 237.4472\n",
      "Epoch 2/2\n",
      "1/1 [==============================] - 16s 16s/step - loss: 1.7488 - val_loss: 4564.2583\n"
     ]
    },
    {
     "data": {
      "text/plain": "<keras.callbacks.History at 0x1e8ff24b8d0>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_gen,epochs=2,validation_data=val_gen,steps_per_epoch=1,validation_steps=1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:25:54.885467Z",
     "start_time": "2025-08-14T06:25:19.070829Z"
    }
   },
   "id": "25bfff93364b3475",
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000001E88A326620> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000001E88A326620> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "31/31 [==============================] - 100s 3s/step\n"
     ]
    }
   ],
   "source": [
    "val_preds = model.predict(val_gen)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:27:35.451852Z",
     "start_time": "2025-08-14T06:25:54.886459Z"
    }
   },
   "id": "539e7998149183dc",
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import PIL\n",
    "\n",
    "\n",
    "def display_mask(i):\n",
    "    # 获取到第i个样本的预测结果\n",
    "    mask = np.argmax(val_preds[i], axis=-1)\n",
    "    # 维度调整\n",
    "    mask = np.expand_dims(mask, axis=-1)\n",
    "    # 转换为图像，并进⾏显示\n",
    "    img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\n",
    "    display(img)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:27:35.457804Z",
     "start_time": "2025-08-14T06:27:35.453340Z"
    }
   },
   "id": "b93edcbfb7636fd0",
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 选中验证集的第10个图像\n",
    "i = 10\n",
    "display(Image(filename=val_input_img_paths[i]))    "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:27:35.465244Z",
     "start_time": "2025-08-14T06:27:35.458797Z"
    }
   },
   "id": "9b3dc5d298ec49b7",
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<PIL.Image.Image image mode=RGB size=279x300>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = PIL.ImageOps.autocontrast(load_img(val_target_img_paths[i]))\n",
    "display(img)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:27:35.475660Z",
     "start_time": "2025-08-14T06:27:35.466237Z"
    }
   },
   "id": "587156ff338f3b8b",
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<PIL.Image.Image image mode=L size=160x160>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_mask(i)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:27:35.484092Z",
     "start_time": "2025-08-14T06:27:35.476652Z"
    }
   },
   "id": "154f0bc609944957",
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "pred=val_preds[10]\n",
    "mask = np.argmax(pred,axis=-1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:30:40.471635Z",
     "start_time": "2025-08-14T06:30:40.467667Z"
    }
   },
   "id": "24d8eedc00e9c7f0",
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0, 0, 0, ..., 0, 0, 0],\n       [2, 2, 2, ..., 0, 2, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       ...,\n       [2, 2, 2, ..., 0, 2, 0],\n       [0, 0, 0, ..., 0, 2, 0],\n       [2, 2, 2, ..., 2, 2, 2]], dtype=int64)"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:30:46.828365Z",
     "start_time": "2025-08-14T06:30:46.823404Z"
    }
   },
   "id": "de292250cfe7f78c",
   "execution_count": 30
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "32a4edbe1e067586"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "model.save('D:/project/heimabigdata/计算机视觉(CV)/lesson5/图像分割/model.h5')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-08-14T06:35:13.684458Z",
     "start_time": "2025-08-14T06:35:13.473658Z"
    }
   },
   "id": "9c37fc8461274072",
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "54382e2f572779f"
  }
 ],
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